Energy transfer in N-component nanosystems enhanced by pulse-driven vibronic many-body entanglement

Scientific Reports

The processing of energy by transfer and redistribution, plays a key role in the evolution of dynamical systems. At the ultrasmall and ultrafast scale of nanosystems, quantum coherence could in principle also play a role and has been reported in many pulse-driven nanosystems (e.g. quantum dots and even the microscopic Light-Harvesting Complex II (LHC-II) aggregate). Typical theoretical analyses cannot easily be scaled to describe these general N-component nanosystems; they do not treat the pulse dynamically; and they approximate memory effects. Here our aim is to shed light on what new physics might arise beyond these approximations. We adopt a purposely minimal model such that the time-dependence of the pulse is included explicitly in the Hamiltonian. This simple model generates complex dynamics: specifically, pulses of intermediate duration generate highly entangled vibronic (i.e. electronic-vibrational) states that spread multiple excitons – and hence energy – maximally within the system. Subsequent pulses can then act on such entangled states to efficiently channel subsequent energy capture. The underlying pulse-generated vibronic entanglement increases in strength and robustness as N increases.

Fernando Gómez-Ruiz, Oscar Acevedo, Ferney Rodríguez, Luis Quiroga, Neil Johnson

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Cavity-induced switching between Bell-state textures in a quantum dot

Physical Review B

Nanoscale quantum dots in microwave cavities can be used as a laboratory for exploring electron-electron interactions and their spin in the presence of quantized light and a magnetic field. We show how a simple theoretical model of this interplay at resonance predicts complex but measurable effects. New polariton states emerge that combine spin, relative modes, and radiation. These states have intricate spin-space correlations and undergo polariton transitions controlled by the microwave cavity field. We uncover novel topological effects involving highly correlated spin and charge density that display singlet-triplet and inhomogeneous Bell-state distributions. Signatures of these transitions are imprinted in the photon distribution, which will allow for optical read-out protocols in future experiments and nanoscale quantum technologies.

Santiago Steven Beltrán Romero, Ferney Rodriguez, Luis Quiroga, Neil Johnson

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Rise of post-pandemic resilience across the distrust ecosystem

Scientific Reports

Why does online distrust (e.g., of medical expertise) continue to grow despite numerous mitigation efforts? We analyzed changing discourse within a Facebook ecosystem of approximately 100 million users who were focused pre-pandemic on vaccine (dis)trust. Post-pandemic, their discourse interconnected multiple non-vaccine topics and geographic scales within and across communities. This interconnection confers a unique, system-level (i.e., at the scale of the full network) resistance to mitigations targeting isolated topics or geographic scales—an approach many schemes take due to constrained funding. For example, focusing on local health issues but not national elections. Backed by numerical simulations, we propose counterintuitive solutions for more effective, scalable mitigation: utilize “glocal” messaging by blending (1) strategic topic combinations (e.g., messaging about specific diseases with climate change) and (2) geographic scales (e.g., combining local and national focuses).

Lucia Illari, Nicholas Johnson Restrepo, Neil Johnson

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Shockwavelike Behavior across Social Media

Physical Review Letters

Online communities featuring “anti-X” hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scales. Its shockwavelike solutions explain how, why, and when such activity rises from “out-of-nowhere,” and show how it can be delayed, reshaped, and even prevented by adjusting the online collective chemistry. This theory and findings should also be applicable to anti-X activity in next-generation ecosystems featuring blockchain platforms and Metaverses.

Pedro Manrique, Frank Yingjie Huo, Sara El Oud, Minzhang Zheng, Lucia Illari, and Neil Johnson

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Stochastic Modeling of Possible Pasts to Illuminate Future Risk

Oxford Academic

Disasters are fortunately uncommon events. Far more common are events that lead to societal crises, which are notable in their impact, but fall short of causing a disaster. Such near-miss events may be reimagined through stochastic modeling to be worse than they actually were. These are termed downward counterfactuals. A spectrum of reimagined events, covering both natural and man-made hazards, are considered. Included is a counterfactual version of the Middle East Respiratory Syndrome (MERS). Attention to this counterfactual coronavirus in 2015 would have prepared the world better for COVID-19.

Gordon Woo, Neil Johnson

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Offline events and online hate

PLOS One

Online hate speech is a critical and worsening problem, with extremists using social media platforms to radicalize recruits and coordinate offline violent events. While much progress has been made in analyzing online hate speech, no study to date has classified multiple types of hate speech across both mainstream and fringe platforms. We conduct a supervised machine learning analysis of 7 types of online hate speech on 6 interconnected online platforms. We find that offline trigger events, such as protests and elections, are often followed by increases in types of online hate speech that bear seemingly little connection to the underlying event. This occurs on both mainstream and fringe platforms, despite moderation efforts, raising new research questions about the relationship between offline events and online speech, as well as implications for online content moderation.

Yonatan Lupu, Richard Sear, Nicolas Velásquez, Rhys Leahy, Nicholas Johnson Restrepo, Beth Goldberg, Neil Johnson

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Losing the battle over best-science guidance early in a crisis: COVID-19 and beyond

Science Advances

Ensuring widespread public exposure to best-science guidance is crucial in any crisis, e.g., coronavirus disease 2019 (COVID-19), monkeypox, abortion misinformation, climate change, and beyond. We show how this battle got lost on Facebook very early during the COVID-19 pandemic and why the mainstream majority, including many parenting communities, had already moved closer to more extreme communities by the time vaccines arrived. Hidden heterogeneities in terms of who was talking and listening to whom explain why Facebook’s own promotion of best-science guidance also appears to have missed key audience segments. A simple mathematical model reproduces the exposure dynamics at the system level. Our findings could be used to tailor guidance at scale while accounting for individual diversity and to help predict tipping point behavior and system-level responses to interventions in future crises.

Lucia Illari, Nicholas J. Restrepo, Neil F. Johnson

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Dynamic Topic Modeling Reveals Variations in Online Hate Narratives

Intelligent Computing

Online hate speech can precipitate and also follow real-world violence, such as the U.S. Capitol attack on January 6, 2021. However, the current volume of content and the wide variety of extremist narratives raise major challenges for social media companies in terms of tracking and mitigating the activity of hate groups and broader extremist movements. This is further complicated by the fact that hate groups and extremists can leverage multiple platforms in tandem in order to adapt and circumvent content moderation within any given platform (e.g. Facebook). We show how the computational approach of dynamic Latent Dirichlet Allocation (LDA) may be applied to analyze similarities and differences between online content that is shared across social media platforms by extremist communities, including Facebook, Gab, Telegram, and VK between January and April 2021. We also discuss characteristics revealed by unsupervised machine learning about how hate groups leverage sites to organize, recruit, and coordinate within and across such online platforms.

Richard Sear, Nicholas Johnson Restrepo, Yonatan Lupu, Neil F. Johnson

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Connectivity Between Russian Information Sources and Extremist Communities Across Social Media Platforms

Frontiers in Political Science

The current military conflict between Russia and Ukraine is accompanied by disinformation and propaganda within the digital ecosystem of social media platforms and online news sources. One month prior to the conflict’s February 2022 start, a Special Report by the U.S. Department of State had already highlighted concern about the extent to which Kremlin-funded media were feeding the online disinformation and propaganda ecosystem. Here we address a closely related issue: how Russian information sources feed into online extremist communities. Specifically, we present a preliminary study of how the sector of the online ecosystem involving extremist communities interconnects within and across social media platforms, and how it connects into such official information sources. Our focus here is on Russian domains, European Nationalists, and American White Supremacists. Though necessarily very limited in scope, our study goes beyond many existing works that focus on Twitter, by instead considering platforms such as VKontakte, Telegram, and Gab. Our findings can help shed light on the scope and impact of state-sponsored foreign influence operations. Our study also highlights the need to develop a detailed map of the full multi-platform ecosystem in order to better inform discussions aimed at countering violent extremism.

Rhys Leahy, Nicholas Johnson Restrepo, Richard Sear, Neil F. Johnson

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